Discriminative segmentation approaches are capable of providing reliable, fully automatic, and fast detection of anatomical landmarks within volumetric (3D) medical images. Discriminative segmentation approaches are also capable of providing accurate determination of organ boundaries, such as boundaries of the inner and outer walls of the heart or a boundary of the liver, in volumetric medical images. Typically, a surface segmented using such discriminative segmentation techniques is represented by a relatively low number of control points, such that the control points can be used in Active Shape Models (ASM).
In addition to restrictions in topology, another disadvantage of such point-cloud based shape representations is the dependence of the local detailedness on the local density of control points. The control points are often non-homogeneously distributed across the shape boundary, and thus yield varying levels of segmentation accuracy. Level set based shape representations, on the other hand, are capable of encoding segmented boundaries at a homogenous resolution, with simple up-sampling and down-sampling schemes, and may provide other advantages over point cloud shape representations as well.